`tf.nn.space_to_depth`.

    ```python
    images = tf.reshape(images, [batch, h // block_size, block_size,
                               w // block_size, block_size, c])
    images = tf.transpose(images, [0, 1, 3, 2, 4, 5])
    images = tf.reshape(images, [batch, h // block_size, w // block_size,
                               c * (block_size ** 2)])
    ```

    `SpaceToDepthOp` can be called on the host to perform the transform.

                        const std::vector<Output>& grad_inputs,
                        std::vector<Output>* grad_outputs) {
  int block_size;
  TF_RETURN_IF_ERROR(
      GetNodeAttr(op.node()->attrs(), "block_size", &block_size));
  grad_outputs->push_back(
      BatchToSpace(scope, grad_inputs[0], op.input(1), block_size));
  grad_outputs->push_back(NoGradient());
  return scope.status();
}

pkg crypto/cipher, type Block interface { BlockSize, Decrypt, Encrypt }
pkg crypto/cipher, type Block interface, BlockSize() int
pkg crypto/cipher, type Block interface, Decrypt([]uint8, []uint8)
pkg crypto/cipher, type Block interface, Encrypt([]uint8, []uint8)
pkg crypto/cipher, type BlockMode interface { BlockSize, CryptBlocks }
pkg crypto/cipher, type BlockMode interface, BlockSize() int

                                TF_Status* status)>
      BlockFetcher;

  RamFileBlockCache(size_t block_size, size_t max_bytes, uint64_t max_staleness,
                    BlockFetcher block_fetcher,
                    std::function<uint64_t()> timer_seconds = TF_NowSeconds)
      : block_size_(block_size),
        max_bytes_(max_bytes),
        max_staleness_(max_staleness),
        block_fetcher_(block_fetcher),

    if (input_batch != ShapedType::kDynamic &&
        input_batch % (block_size * block_size) != 0) {
      return op.emitOpError()
             << "requires input batch (dimension 0) to be evenly divisible "
                "by (block_size * block_size), but got input batch "
             << input_batch << " and block_size " << block_size;
    }

    input_shape.assign(input_type.getShape().begin(),

  TensorShape y_shape({1, 1, 1, 4});
  auto y = SpaceToDepth(scope_, x, /* block_size */ 2);
  RunTest(x, x_shape, y, y_shape);
}

TEST_F(ArrayGradTest, DepthToSpaceGrad) {
  TensorShape x_shape({1, 1, 1, 4});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
  TensorShape y_shape({1, 2, 2, 1});
  auto y = DepthToSpace(scope_, x, /* block_size */ 2);
  RunTest(x, x_shape, y, y_shape);
}

def LegalizeSpaceToDepth : Pat<
  (TF_SpaceToDepthOp $input, $block_size, IsDataFormatNHWC:$data_format),
  (TFL_SpaceToDepthOp $input, (convertIntAttrTo32Bit $block_size))>;

def LegalizeDepthToSpace : Pat<
  (TF_DepthToSpaceOp $input, $block_size, IsDataFormatNHWC:$data_format),
  (TFL_DepthToSpaceOp $input, (convertIntAttrTo32Bit $block_size))>;

def LegalizeResizeBilinear : Pat<

package zstd

import (
	"math/bits"
)

// block is the data for a single compressed block.
// The data starts immediately after the 3 byte block header,
// and is Block_Size bytes long.
type block []byte

// bitReader reads a bit stream going forward.
type bitReader struct {
	r    *Reader // for error reporting
	data block   // the bits to read
	off  uint32  // current offset into data

                  not specified or empty, defaults to ["Tunnel", "Workload"] for back-compatibility
                items:
                  type: string
                type: array
              blockSize:
                description: The block size to use for IP address assignments from
                  this pool. Defaults to 26 for IPv4 and 122 for IPv6.
                type: integer
              cidr:

  "ArrayRef<APInt>{$0.getValue(), $0.getValue()})">;

def BatchToSpaceToBatchToSpaceND : Pat<
  (TF_BatchToSpaceOp:$src $input, $crops, $block_size),
  (TF_BatchToSpaceNDOp:$dest $input,
    (TF_ConstOp (BatchToSpaceBlockSizeToBlockShape $block_size)), $crops),
  [(IsRank4Tensor $input), (IsRank2Tensor $crops)], [(CopyAttrs $src, $dest)]>;